Using computer vision to identify mosquito larvae species
Official title: Automated identification of larval mosquito species with computer vision
Funded at Vectech, LLC · NIH-11075797 · Data updated 2026-11-12
This study is working on a smart system that uses computer technology to quickly identify different types of mosquito larvae, helping to improve mosquito control efforts and keep you safe from bites.
Quick facts
| Grant type | Sbir 1 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Vectech, LLC NIH-funded |
| Lab location | 1 site (Baltimore, UNITED STATES) |
| Project start | April 16, 2024 |
| Project end | March 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $107,987 |
| NIH project number | 5R43AI184319-02 |
| Project ID | NIH-11075797 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals living in areas prone to mosquito-borne diseases, particularly those under 21 years old.
✕Less likely to help
Patients who do not reside in mosquito-prone areas or who are not at risk of mosquito-borne diseases may not benefit from this research.
What this research studies
This research focuses on developing an automated system that uses computer vision technology to identify different species of mosquito larvae. By monitoring breeding sites and understanding the composition and abundance of these larvae, the project aims to enhance mosquito control efforts. The approach involves collecting larval samples and utilizing artificial intelligence algorithms to classify them accurately, which can lead to timely interventions before adult mosquitoes emerge. This method seeks to overcome the limitations of traditional larval surveillance, which is often resource-intensive and prone to delays.
Why it matters
Potential benefit: If successful, this research could significantly improve mosquito control strategies, reducing the incidence of mosquito-borne diseases.
How similar studies have gone: While there have been efforts to use crowdsourcing and unmanned aircraft systems for mosquito surveillance, this automated identification approach is relatively novel and untested.
Where this research is happening
-
Vectech, LLC
Baltimore, United States Active
Researchers
- Principal investigator: Faiman, Roy — Vectech, LLC
- Study coordinator: Faiman, Roy
Related research
Other NIH-funded projects on the same topics.
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Automated identification of tick species using advanced technology
This study is working on a smart computer system that can quickly and accurately identify different types of ticks, which will help keep track of them and prevent diseases like Lyme disease, making it easier for everyone to stay healthy in areas where ticks are common.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.